Multidimensional data visualizations using R
 

SIMP59: Data Selection and Visualisation, 7.5 credits VT25

nils.holmberg@iko.lu.se

Overview

  • 5.1 Amounts
  • 5.2 Distributions
  • 5.3 Proportions
  • 5.4 x–y relationships
  • 5.5 Geospatial data
  • 5.6 Uncertainty
  • subplots, facets
  • 3d plots ?
  • tidyverse
  • dplyr (selection, wrangling)
  • selection or data tranformation?
  • ggplot2 (data visualization)

Course literature

Wickham, Çetinkaya-Rundel, and Grolemund (2023)

Wilke (2019)

Palmer Penguins

test

Quantitative methods

    1. Experiments and
      Threats to Validity
    1. Survey Research,
      Questionnaire
    1. Quantitative
      Content Analysis

Lectures and workshops

Data collection (nov 12)

    1. Concept Explication and Measurement
    1. Reliability and Validity
    1. Effective ­Measurement
    1. Sampling
    1. Content Analysis

Exam question 1

Data analysis (nov 26)

    1. Experiments and Threats to Validity
    1. Survey Research
    1. Descriptive Statistics
    1. Inferential Statistics
    1. Multivariate Statistics

Exam question 2

9. Experiments and Threats to Validity

  • Random Assignment (p. 225)
  • Between-Subjects Design (p. 227)
  • Within-Subjects Design (p. 228)
  • Treatment Groups (p. 233)
  • Stimulus (p. 233)
  • Control Group (p. 238)

Next steps

Workshop 2, dec 2

References

Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science. " O’Reilly Media, Inc.".
Wilke, Claus O. 2019. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O’Reilly Media.